Ecological Applications, 14(1), 2004, pp. 296–310
q 2004 by the Ecological Society of America
EVALUATION OF A MULTIPLE-SPECIES APPROACH TO MONITORING
SPECIES AT THE ECOREGIONAL SCALE
PATRICIA N. MANLEY,1,4 WILLIAM J. ZIELINSKI,2 MATTHEW D. SCHLESINGER,1
AND
SYLVIA R. MORI3
1Pacific
Southwest Station, USDA Forest Service, 2121 Second Street, Suite A-101, Davis, California 95616 USA
Pacific Southwest Station, USDA Forest Service, 1700 Bayview Drive, Arcata, California 95521 USA
3Pacific Southwest Station, USDA Forest Service, 800 Buchanan Street, Albany, California 94710 USA
2
Abstract. Monitoring is required of land managers and conservation practitioners to
assess the success of management actions. ‘‘Shortcuts’’ are sought to reduce monitoring
costs, most often consisting of the selection of a small number of species that are closely
monitored to represent the status of many associated species and environmental correlates.
Assumptions associated with such shortcuts have been challenged, yet alternative approaches remain scant. We evaluated an approach that departs significantly from the approach of selecting a few representative species. We explored two primary assertions: (1)
that a coordinated multiple-species monitoring effort that collects presence–absence data
on a broad range of species is a robust alternative to a few intensive single-species efforts,
and (2) that the vertebrate species expected to be detected using this approach are numerous
and diverse enough to represent all vertebrate species. We simulated monitoring the vertebrate species pool on an existing sample grid across the 7 million ha of public lands in
the Sierra Nevada (USA) ecoregion. Based on the use of eight standard presence–absence
protocols, we estimated the number of vertebrate species (excluding fish) with an adequate
number of sample points within their range to detect $20% relative change in the proportion
of points with detections between two points in time. We estimated that adequate detections
would be obtained for 76% of the 465 vertebrate species, including 83% of all birds, 76%
of all mammals, 65% of all reptiles, and 44% of all amphibians. Detection adequacy varied
among life-history and ecological groups, but .50% of the species were adequately detected
in every group with the exception of three groups: rare species, endemic species, and species
of concern (33%, 24%, and 47% of associated species adequately detected, respectively).
A multiple-species monitoring approach represents an effective and feasible alternative to
the challenges of large-scale monitoring needs by targeting the most basic of population
data for a large number and breadth of species.
Key words: biological diversity; conservation strategies, choosing; inventory; land management;
management actions, assessing success of; monitoring; populations; Sierra Nevada (California, USA);
species monitoring, multiple-species approach; survey; vertebrate population distributions.
INTRODUCTION
It has been over 25 years since the passage of the
Endangered Species Act (1973) and National Forest
Management Act (1976).5 Since that time, scientists
and land managers have been challenged with the task
of understanding, maintaining, and monitoring biological diversity and ecosystem integrity (Mooney et al.
1995a, b, Christensen et al. 1996, DeLeo and Levin
1997, Kinzig et al. 2002). Monitoring to assess the
success of management activities in meeting legal, regulatory, and policy objectives is required of land-management agencies. Land managers often look for
‘‘shortcuts’’ in the absence of funding to conduct all
monitoring that would ideally describe the condition
Manuscript received 29 July 2002; revised 11 April 2003;
accepted 23 April 2003; final version received 27 May 2003.
Corresponding Editor: M. G. Turner.
4 E-mail: pmanley@fs.fed.us
5 Endangered Species Act of 1973, U.S. Code title 16,
sections 1531–1544; National Forest Management Act of
1976, U.S. Code title 16, sections 1600–1614.
of lands and associated biota to inform management
decisions (Tracy and Brussard 1994, Fleishman et al.
2000). In the case of monitoring species diversity, a
prominent shortcut is the proposal that the status of a
small set of carefully chosen individual species can
represent the integrity of the entire ecosystem (Thomas
1972, Noss 1990, Frost et al. 1992, Stolte and Mangis
1992, Stohlgren et al. 1995, Oliver and Beattie 1996,
Dufrene and Legendre 1997, Lambeck 1997, Longino
and Colwell 1997, Niemi et al. 1997, Simberloff 1998).
The impetus for such a shortcut comes from the recognition that it is infeasible to monitor all species, and
conservation goals and management objectives for biological diversity and ecosystem integrity cannot be
met by focusing on one species at a time (Franklin
1993, Wilcove 1993).
Various conceptual approaches have been offered as
means to create shortcuts, which can be assigned to
two broad groups. The first seeks to identify correlations between the patterns of a target variable and a
proxy variable—if two variables are highly correlated,
296
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MULTIPLE-SPECIES APPROACH TO MONITORING
one can infer the dynamics of one by monitoring the
other. Concepts that fit within this first approach include ‘‘umbrella’’ species (Wilcox 1984, Fleishman et
al. 2000) and ‘‘indicator’’ species (Landres et al. 1988,
Niemi et al. 1997), as well as tools such as wildlife
habitat models (e.g., Mayer and Laudenslayer 1988).
The second approach seeks to identify species that play
key roles in ecosystem function. Identification of ‘‘keystone’’ species (Bond 1995, Power et al. 1996, Simberloff 1998), and ‘‘ecosystem engineers’’ (Jones et al.
1994) are examples of this second approach. Then there
is the ‘‘flagship’’ species concept (Western 1987, Simberloff 1998), which is modestly related to umbrellaand indicator-species concepts, but which is largely
based in sociology rather than ecology. Caro and
O’Doherty (1999) clarify the use of, and criteria for,
many of these contemporary designations, and recent
work has provided additional criteria for representatives (e.g., Fleishman et al. 2000). However, all of these
approaches assume that the status of a few species or
other ecosystem parameters can indicate the abundance
or distribution of other species or the condition of an
ecosystem, and this assumption has been widely challenged (Verner 1984, Landres et al. 1988, Strong 1990,
Niemi et al. 1997, Swanson 1998, Lindenmayer et al.
2002).
Although the notion of monitoring a few select species has appeal from a practical perspective, the absence of complete knowledge of species’ ecologies and
their functional roles in ecosystems means that indicator approaches should be viewed as hypotheses to
test (Caro and O’Doherty 1999, Committee of Scientists 1999). We view this approach as a few-eggs-inone-basket alternative because agencies typically
choose a few indicators whose relationships to ecosystem condition or function are presumed to be known
and then invoke expensive programs to track detailed
population parameters (e.g., absolute population size,
population growth rates, behavior patterns) (e.g.,
USDA Forest Service 1997a), resulting in tremendous
financial investment per species. The primary risks of
this approach are that: (1) the few chosen species may
not represent any other species or show strong relationships with environmental conditions affected by
management, (2) species chosen as indicators today
may not serve as indicators of current stressors or future threats, and (3) despite huge investments in monitoring individual species, uncertainty and difficulty
may thwart attempts to translate population-based results into an appropriate system-based interpretation
and response.
Growing threats to biological diversity and ecosystem integrity call for innovative approaches for meeting
current conservation challenges. We evaluate a multiple-species approach that targets basic presence–absence data on a large number and breadth of species—
essentially a many-eggs-in-many-baskets alternative.
By recording the occurrence of species at sample points
297
across an ecoregion, a multiple-species approach simply monitors change in the proportion of sites occupied
by individual species. Our logic argues that the larger
the proportion of all species represented in a sample,
the greater the likelihood that the sample accurately
reflects the sum total of all species.
We view the proportion of sample points at which a
species is detected as an index of regional occurrence,
assuming that it is unlikely that significant population
increase or decrease can occur without some change in
the proportion of sites where a species is detected.
Monitoring the change in regional occurrence of a species is not as informative as direct estimates of abundance for a given species, but this approach deliberately
targets a large number and breadth of species at the
expense of more detailed population data. Similar approaches to population monitoring based on the extent
of a species’ occurrence have been proposed using atlas
data (e.g., Pearman 1997, Telfer et al. 2002). Although
the index of status and change for each species is crude,
the areal extent of a population and its size often have
a positive relationship (e.g., Nachman 1981, Geissler
and Fuller 1986, Bart and Klosiewski 1989, Robbins
et al. 1989, Gaston 1994, Syrjala 1996, Thompson et
al. 1998).
A multiple-species monitoring approach does not require a priori knowledge about ecological function of
individual species; rather these data could yield valuable information on spatial and temporal covariance
relationships among species and between species and
their environment. Examining change in the status of
groups of species with different characteristics enables
the investigator to develop hypotheses about environmental factors associated with, and perhaps responsible
for, changes in species occurrence. Thus, we also evaluated the multiple-species approach by determining
how the species predicted to be adequately sampled are
distributed among classes of phylogeny, life-history
traits, habitat associations, habitat specificity, trophic
levels, and rarity.
We asked this basic question: If a set of survey protocols were conducted at an array of monitoring stations across an ecoregion at two points in time, for
which vertebrate species would we be able to detect a
minimum magnitude of change in occurrence at a minimum level of precision? We estimated how many and
which vertebrate species would be adequately sampled
using commonly employed detection methods at a given sample of points, based on the probability of detection and the number of points falling within the geographic range of each species. The simulation was intended to explore two primary assertions: (1) that a
coordinated multiple-species monitoring effort that
collects presence–absence data on a broad range of
species is a robust alternative to a few intensive singlespecies efforts, and (2) that the vertebrate species expected to be detected using this approach are numerous
and diverse enough to represent all vertebrate species.
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Vol. 14, No. 1
PATRICIA N. MANLEY ET AL.
298
FIG. 1. Location and bounds of the Sierra Nevada study
area in California and Nevada, USA.
By constructing and simulating a sampling scenario
with specified effort and precision, we evaluated the
ability of the approach to provide sufficient data on
vertebrate species to confirm these assertions.
MATERIALS
AND
METHODS
Study system
We based our evaluation on the geographic area encompassing the Sierra Nevada and Southern Cascade
ranges in California, USA (Fig. 1), identified by the
Sierra Nevada Ecosystem Project (1996) as the extent
of the greater Sierra Nevada ecoregion. The area serves
as an illustrative case study for three primary reasons:
(1) it is a distinct ecoregion, being a mountain range
juxtaposed to desert, valley, and Great Basin ecosystems; (2) it has high biological diversity (Ricketts et
al. 1999), and (3) concerns are mounting for the fate
of its biological diversity as affected by a combination
of past and present land-use practices, increasing pressure from a growing human population in California,
and rising recreational use (Duane 1996, Sierra Nevada
Ecosystem Project 1996, USDA 2001). The Sierra Nevada ecoregion covers over 12 3 106 ha with ;60%
consisting of public lands, and 465 species of vertebrates (excluding fish) have some or all of their geographic range within the bounds of the analysis area
(USDA Forest Service 2001).
Vertebrate species were selected for the simulation
because the breadth of standardized and tested survey
techniques for vertebrates made it more feasible to estimate their effectiveness at detecting species. Further,
the availability of life-history information on each species made it possible to assess the information conveyed by species predicted to be adequately detected.
We selected a set of eight standardized, commonly employed, nonlethal, multiple-species detection methods
to be applied at sample locations to maximize the number of vertebrate species that would be detected (Table
1). The assumed effort expended in each protocol (e.g.,
number of traps, number of visits, area searched) followed generally prescribed levels for maximizing detectability with reasonable effort (Table 1).
Simulated sample design
We used a systematic grid of points based on the
national Forest Inventory and Analysis (FIA) program
TABLE 1. Protocols selected to simulate detection rates for vertebrates in the Sierra Nevada, California, USA, using the
multiple-species monitoring approach.
Protocol†
Point counts
Effort
Reference
Target taxa
7 stations, 10-min counts, 3
visits
2 visits
Ralph et al. (1993, 1995)
233 birds and 3 mammals
Fuller and Mosher (1981)
100 traps, 4 nights
Jones et al. (1996)
50 traps, 4 nights
Jones et al. (1996)
Track stations with cameras
6 stations, 16 days
Zielinski and Kucera (1995)
Area searches for vertebrates and their sign
10-ha area, 2 visits
Crump and Scott (1994),
Wemmer et al. (1996)
Mist netting and acoustic
surveys
Aquatic visual encounter
surveys
3 net sites, 6 visits
Jones et al. (1996)
13 nocturnal birds, including
owls
67 mammals, all of them
small terrestrial species
14 mammals, all of them
small to mid-sized terrestrial species
17 mammals, all mid-sized
carnivores
12 terrestrial amphibians, 50
terrestrial reptiles, and 12
mammals, including large
rodents and all ungulates
17 mammals, all bats
2 visits
Crump and Scott (1994),
Fellers and Freel (1995)
Broadcast calling (nocturnal)
Live trapping (Shermanlong traps)
Live-trapping (Tomahawk
traps)
20 amphibians, 5 reptiles,
and 2 mammals, all primarily aquatic
† These eight standardized protocols are commonly employed, nonlethal, multiple-species detection methods.
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MULTIPLE-SPECIES APPROACH TO MONITORING
(Roesch and Reams 1999) to represent the location and
density of monitoring sites. The National Forest Management Planning Act of 1976 authorized and promoted a nationwide survey and analysis of all renewable natural resources (Frayer and Furnival 1999); one
result was the establishment of the FIA program. The
current design consists of sample points located in a
systematic hexagonal grid (centers of each hexagon
spaced 5.4 km apart) across all ownerships in the United States, and vegetation structure and composition are
scheduled to be described at each point once every 10
years (Roesch and Reams 1999). We selected the FIA
grid as the basis of our evaluation because it promised
a temporally and spatially reliable source of vegetation
data across land ownerships, and thus constituted an
attractive system on which to build species-monitoring
schemes. Further, the density of the grid was low
enough to assume independence between points for
species with all but the largest home ranges (Zeiner et
al. 1988, 1990a, b). We created a mock hexagonal grid
using the spacing parameters of the actual FIA grid
and, based on the location of the center point of each
hexagon, we estimated that ;2760 FIA grid points occurred on Federal lands in the ecoregion. These points
were treated as the set of potential monitoring sites.
Through a series of steps described below, we estimated the number of species that had at least the
minimum number of sample points within their range
sufficient to detect a given magnitude of change between two points in time (i.e., detection of decrease or
increase; two-tailed test) at a set level of precision. We
tested the null hypothesis that a change of a given
magnitude or greater occurring between two sample
periods could not be detected, vs. the alternative that
the specified change could be detected. We chose to
base the evaluation on the comparison of two points
in time vs. alternative scenarios (e.g., trend analysis
based on multiple sample periods) because such a comparison represents the most modest monitoring objective. For the purposes of this evaluation, we chose a
relative change of $20% in the proportion of sites with
observations of a species as a minimum effect size, d,
given that a change of this magnitude would certainly
signal cause for concern for most species and could
represent a great risk to viability for species already of
concern.
Probability of presence and detection
We estimated the number of sites at which we expected to detect each species during the first sample
period by evaluating the probability of observing at
least one individual at a given location within its range.
The proportion of monitoring sites at which a species
is observed, the probability of observation during a
sample period t (Pt), is a function of the species’ (1)
probability of presence (pp), and (2) probability of detection if present (pd). The relationship between the
three probabilities is as follows:
Pt 5 pp 3 pd.
299
(1)
The probability of presence, pp, for each species was
estimated based on the proportion of the species’ range
in the study area that was comprised of suitable habitat.
Maps of the approximate extent of the range of each
species in the Sierra Nevada were obtained from the
California wildlife habitat relationships (CWHR) Program (CDFG 2000), and then range boundaries were
updated using expert opinion. We used definitions of
suitable habitat obtained from the CWHR database and
habitat maps for the Sierra Nevada were obtained from
interpreted satellite imagery (USDA Forest Service
1997b). Habitat types in the CWHR Program are defined by a combination of vegetation series, seral stage,
and canopy-cover values. We overlaid the habitat maps
onto the estimated species’ ranges to calculate the proportion of the area within each species’ range comprised of suitable habitat (Fig. 2). Species were assigned one of three levels of pp: those with ,30% of
their range comprised of suitable habitat types were
assigned a low pp (modeled as pp 5 0.10), those with
30–70% of their range comprised of suitable habitat
types were assigned a moderate pp (modeled as pp 5
0.5), and those with .70% of their range comprise of
suitable habitat types were assigned a high pp (modeled
as pp 5 0.80).
We created three levels of pp instead of evaluating
it as a continuous variable because our estimates were
crude and the information available did not justify attempts at greater resolution. Our calculation of the proportion of the range occupied by each species is likely
to overestimate the probability of presence in that it
implicitly assumes suitable habitat is occupied. The
potential optimism of our estimates was largely mitigated by assigning a probability-of-presence interval
to each species as opposed to using the specific calculated values. Further, we evaluated the effects of potentially inflated pp on the outcome of our evaluation.
We then assigned one of three levels of probability
of detection given presence, pd (high, moderate, and
low, modeled as 0.8, 0.5, and 0.1, respectively), based
on characteristics such as visibility and ease of identification using the typical survey methods. Assigned
values were based on published literature and the professional experience of multiple taxonomic experts and
local researchers. For example, if we designated a species as having a moderate pd, our assumption was that
employing the detection methods over a reasonable
area and for a reasonable duration where the species
is present would result in detecting at least one individual of that species 50% of the time. Our determinations were routinely conservative (i.e., when in
doubt, we assigned lower pd values), resulting in a potential bias toward an overestimate of requisite minimum sample sizes.
Precision parameters and sampling adequacy
Minimum sample size requirements were influenced
by the following factors: (1) the proportion of moni-
PATRICIA N. MANLEY ET AL.
300
Ecological Applications
Vol. 14, No. 1
FIG. 2. Example calculation of number of monitoring stations within a species’ range and probability of presence (pp),
the proportion of the range occupied by suitable habitat.
toring stations estimated to have observations during
the first sample period (P1), (2) the effect size (d), (3)
the prescribed error rates (a and b), and (4) the direction of change desired to detect (one- or two-tailed)
(Sokal and Rohlf 1995). The relationship between the
proportion of points with detections before ( P1) and
after (P2) given a decline of a given effect size (d) is
as follows:
P2 5 P1 3 (1 2 d).
(2)
Thus, if P1 5 0.25, then a $20% relative decrease in
P1 equates to a P2 of #0.20.
We selected a # 0.2, or 80% confidence ([1 2 a]
3 100 5 percentage confidence), as an acceptable Type
I error rate; the likelihood of incorrectly rejecting the
null hypothesis. The Type II error rate, the likelihood
of incorrectly accepting the null hypothesis, was set at
0.20, or statistical power of 80% (power 5 [1 2 b] 3
100). A two-tailed test was selected because it offers
the greatest flexibility in data analysis and provides a
more rigorous test of the detection adequacy. The minimum number of samples (N*) necessary to detect
change between two sample periods for a given species
was estimated using the normal approximation (Fleiss
1981), and is given by the following inequality:
N* .
(za 3 s 0 1 zb 3 s a ) 2
(d 3 P1 ) 2
(3)
where za and zb represent the two-tailed critical values
from a normal distribution, and s0 and sa represent
standard deviations of the difference between P1 and
P2 under the null and alternative hypothesis, respectively (Fleiss 1981). We assumed that the site correlation between sample periods was high (0.90) but not
perfect (1.0) because although all the same sites were
being remeasured, some errors in relocation during the
second sample period could occur (Hoel et al. 1971a).
We calculated variance based on a binomial distribution, and then calculated minimum sample sizes based
on the assumption that the observed Pt was approximately normally distributed (Hoel et al. 1971a, b). Variance associated with binomial distributions is greatest
at the midpoint (0.50) and tapers toward 0 and 1 from
the midpoint (Zar 1984). Given that we used a twotailed test, variance and associated sample size requirements were asymmetrical, with the larger value associated with increases when P1 , 0.5 and declines when
P1 . 0.5. We always selected the larger of the two
sample-size estimates to increase the rigor of our evaluation.
We calculated the minimum sample size requirement, N*, for each species, and then compared it to the
number of FIA monitoring sites within that species’
range. If N* for a given species was less than the number of monitoring stations in its range, we concluded
that the multiple-species monitoring approach would
be adequate to detect change in that species. The end
result of the analysis was a list of species for which
implementing the approach across the Sierra Nevada
would be adequate to detect a $20% change in the
proportion of sites with detections between two sample
periods. We refer to this list of species as the set of
‘‘adequately detected species.’’
Evaluation of adequately detected species
The multiple-species monitoring approach was evaluated in terms of the two basic assertions put forth in
the introduction: (1) multiple-species monitoring can
provide data for assessing population status of a large
number of species at an ecoregional scale, and (2) the
suite of species observed using the approach may serve
to represent species diversity and environmental influ-
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MULTIPLE-SPECIES APPROACH TO MONITORING
ences. We assessed the representativeness of the adequately detected set by evaluating their membership in
a variety of taxonomic, life-history, and ecological
groups. The proportion of each group for which the
sampling effort would be adequate to detect change in
Pt was calculated and summarized.
Taxonomic and life-history groupings included phylogeny, trophic level, home-range size, body mass, and
habitat affiliation, as derived from a variety of sources
(Licht 1965, Kaufman and Gibbons 1975, Burt and
Grossenheider 1976, Lewke 1979, Parmenter 1981,
Dunning 1984, Kline 1985, Ehrlich et al. 1988, Zeiner
et al. 1988, 1990b, Whittier and Crews 1990, Meienberger et al. 1993, Talbot and Feder 1993, Holland
1994; G. Fellers, unpublished data; H. Welsh and A.
Lind, unpublished data; L. Diller, unpublished data).
Taxonomic diversity serves as a coarse proxy for genetic diversity in large-scale assessments, given that
genetic diversity is greater among than within taxonomic groups. We assessed the representation of species within vertebrate orders and classes. Trophic levels
have various responses to environmental changes and
perturbations as a result of different resource needs and
vulnerabilities (Noss et al. 1996, Polis and Winemiller
1996, Terborgh et al. 1999), and we recognized four
distinct trophic levels: carnivore, omnivore, herbivore,
and scavenger. Home-range size has been linked to
vulnerability to population decline (Terborgh 1974).
Body mass and home range tend to be positively related
(Harestad and Bunnell 1979), but not consistently
among taxonomic groups. In an analysis of monitoring
data, trends by home-range size and body-mass categories would be most informative if calculated within
taxonomic groups. However, for the purposes of evaluating representation we simplified the analysis by
looking across taxonomic groups. Four home-range
size categories were established: small (,0.1 ha), medium (0.1–39.9 ha), large (40–500 ha), and extensive
($500 ha). Three categories of body mass were established: small (,0.75 kg), medium (0.75–2.0 kg), and
large (.2.0 kg). Habitat affiliation was represented by
three primary categories: terrestrial, semi-aquatic, and
aquatic. Species were considered aquatic if their entire
life cycle required being in or on water (e.g., Piedbilled Grebe [Podilymbus podiceps], river otter [Lutra
canadensis]). Species were considered semi-aquatic if
one part of their life cycle required water or if at least
a portion of their prey base consisted of aquatic species
(e.g., Belted Kingfisher [Ceryle alcyon], beaver [Castor
canadensis], western toad [Bufo boreas]). The remaining species were considered terrestrial.
Ecological groupings included habitat specificity,
rarity, endemism, exotics, and species of concern, and
were derived from State Natural Heritage Program data
bases (Zeiner et al. 1988, 1990a, b, CDFG 2000). Habitat specialists are often at greater risk of extirpation
than habitat generalists (e.g., Rabinowitz 1981, Kattan
1992, MacNally and Bennett 1997). Three categories
301
TABLE 2. Estimated vertebrate detectability in the Sierra
Nevada, California, USA, study area: (a) the number of
vertebrate species present assigned each combination of
probability of presence and detection levels, and (b) the
number of monitoring stations required within a species’
geographic range for each combination of parameters.
Probability of
presence‡
Probability of detection†
Low
a) Number of species
Low
25
Moderate
23
High
9
Total
57
Moderate
High
Total
52
72
12
136
131
123
18
272
208
218
39
465
b) Number of monitoring stations required within geographic
range
Low
2352
450
272
···
Moderate
450
70
34
···
High
272
34
12
···
† Probability of detection ( pd): low 5 0.10, moderate 5
0.50, high 5 0.80.
‡ Probability of presence ( pp): low 5 0.10, moderate 5
0.50, high 5 0.80.
of relative habitat specificity (low, moderate, and high)
were derived by calculating the proportion of all habitat
types (i.e., vegetation type, seral stage, and canopycover class combinations) occurring in the Sierra Nevada (Mayer and Laudenslayer 1988, CDFG 2000) that
were used by each species. Species were considered
rare if their geographic ranges occupied ,5% of the
Sierra Nevada or had population size of #100 individuals. Population size was estimated by soliciting
information from specialists most familiar with the status of each species. Experts were asked to place each
species in one of five log-scale intervals of population
size ranging from potentially extirpated to .10 000 individuals. We also evaluated Sierra Nevada and California endemics because endemic species contribute
significantly to local biological diversity in that they
occur nowhere else (Meffe and Carroll 1997). Exotic
species, particularly aggressive invasives, can have severe impacts on the biological integrity of an ecosystem
(Soulé 1990, Graber 1996, Cox 1999). The identification and conservation of species that are sensitive or
vulnerable, and thus of concern, is a common component of landscape conservation efforts (e.g., Noss
and Cooperrider 1994). Species of concern were defined as those with one of more of the following designations: Federally Threatened or Endangered, Forest
Service sensitive, and California State Threatened or
Endangered.
RESULTS
General patterns of detectability
Of the 465 vertebrate species evaluated, only 8%
were assigned a high probability of presence ( pp; Table
2a), and 58% were assigned a high probability of detection ( pd). The higher pp or pd, the greater the likelihood that a species was adequately detected (Table
302
PATRICIA N. MANLEY ET AL.
FIG. 3. The relationship between the proportion of points
in a species range with observations at the first time period
(P1) and the number of monitoring stations required to detect
the species, displayed using a logarithmic scale. Four combinations of effect size (d, percentage change) and precision
(prescribed error rates a and b) are reported. Along the line
representing the effect size and precision parameters evaluated in this paper (20% change in the proportion of sites with
observations of a species) we note the sample size requirements for each of six pairwise combinations of low (L), moderate (M), and high (H) probabilities of presence ( pp) and of
detection ( pd ). Correlation between sites sampled in each
period was set at 0.90.
2a). An average of 93% of all species with pd and pp
$ 0.5 were adequately detected. Detection adequacy
declined to an average of around 50% when either pd
or pp was low.
The general relationship between Pt (the probability
of observation), and minimum sample-size was such
that sample-size requirements increased as a power
function as Pt linearly decreased from 1 to 0 (Fig. 3).
Based on the values selected for our evaluation, detecting a 20% relative change in Pt with Type I and
Type II error rates of 20% required 12 monitoring stations when pd and pp were both high, 70 monitoring
stations when pd and pp were both moderate, and over
2300 monitoring stations when pd and pp were both low
(Table 2b).
When we evaluated species in the Sierra Nevada
(USA) based our desired level of precision, we predicted that 76% (n 5 355 species) of the 465 vertebrate
species would be adequately detected (Fig. 4a). Of the
110 species not adequately detected, 27 species had
small ranges encompassing fewer than 13 points, which
was the minimum number of points required to be adequately detected when presence and detection were at
their highest (i.e., pp and pd 5 0.80). Detection of the
remaining 83 species was limited primarily by low pp
or pd. At the assigned levels of pp or pd, only 10 of
these species would be adequately detected to discern
a #20% change in the proportion of points with detections with the addition of #50 more points within
their range; the remaining 73 species would require
Ecological Applications
Vol. 14, No. 1
even more points to be adequately detected, ranging
from 50 to .2000 additional points (Fig. 4b).
Each of the statistical parameters of the evaluation
had substantial effects on the number of species estimated to be adequately detected. We compared our selected effect size of 20% to that of 10%, and our selected error rate of 20% to that of 10% and 5%. For a
given effect size, reductions in error rate resulted in 27
to 52 fewer species being adequately detected (Fig. 5).
However, reductions in effect size had the greatest influence on the number of adequately detected species.
At a given error rate, changing the effect size from
20% to 10% resulted in an average of 115 fewer species
being adequately detected, and 32 fewer species were
adequately detected when effect size was 10% and error
rates were at their highest (20%) than when effect size
was 20% and error rates were at their lowest (5%) (Fig.
5). Altering the assigned values of probability of presence and detection to reflect potential design changes
did not have a substantial effect on species detections.
Given the potential overestimate of probability of presence, we looked at the effect of reducing probability
of presence to a maximum of 0.50, which resulted in
only three fewer species being adequately detected. We
also evaluated the potential gains associated with increasing probability of detection to 0.5 for species with
FIG. 4. Summary of predicted detection adequacy for vertebrate species in the Sierra Nevada (USA) based on the multiple-species monitoring approach: (a) proportion of species
predicted to be adequately detected based on detecting $20%
relative change between two points in time with 80% confidence and power; (b) the number of additional points necessary to adequately detect species with low probability of
presence ( pp) or of detection ( pd ).
MULTIPLE-SPECIES APPROACH TO MONITORING
February 2004
303
low detectability, which resulted in only 14 more species being adequately detected.
Representation
FIG. 5. Predicted number of vertebrate species adequately
detected at two effect sizes and three levels of error rates a
and b based on the comparison of two points in time using
the multiple-species monitoring approach.
The multiple-species monitoring approach was predicted to adequately detect $20% change with 20%
error for the majority of species in three of the four
vertebrate classes: 83% (n 5 205 species) of birds, 76%
(n 5 100 species) of mammals, 65% ( n 5 36 species)
of reptiles, and 44% (n 5 14 species) of amphibians
(Table 3). Also, all but one order of vertebrates (Perissodactyla) was represented by one or more species
based on expected detections (Table 3). Particularly
well represented ($85% of all taxa) among birds and
mammals were the avian orders Charadriiformes, Falconiformes, Strigiformes, Caprimulgiformes, Coraciiformes, and Piciformes, and the mammalian orders
Chiroptera, Lagomorpha, and Marsupalia. Orders with
few species were less well represented (#67%), namely
the avian orders Gruiformes, Pelecaniformes, Cuculiformes, Galliformes, and Ciconiiformes and the mammalian orders Insectivora and Perissodactyla (Table 3).
TABLE 3. Predicted adequacy of the multiple-species monitoring approach in detecting $20% relative change (with 80%
confidence and power) in the occurrence of vertebrate species by taxonomic class and order in the Sierra Nevada, California,
USA.
No. of vertebrate species
Adequately
detected
Not adequately
detected
Total
Proportion
adequately
detected
14
8
6
18
7
11
32
15
17
0.44
0.53
0.35
Birds (Aves)
Birds of prey (Falconiformes)
Cranes and rails (Gruiformes)
Cuckoos and allies (Cuculiformes)
Fowl-like birds (Galliformes)
Goatsuckers and allies (Caprimulgiformes)
Herons and allies (Ciconiiformes)
Kingfishers (Coraciiformes)
Owls (Strigiformes)
Pelicans and cormorants (Pelecaniformes)
Perching birds (Passeriformes)
Pigeons (Columbiformes)
Shorebirds and gulls (Charadriiformes)
Swifts and hummingbirds (Apodiformes)
Waterfowl (Anseriformes)
Woodpeckers (Piciformes)
205
14
2
1
6
3
6
1
10
1
108
3
13
8
15
11
41
0
2
1
3
0
4
0
1
1
20
1
1
2
3
2
246
14
4
2
9
3
10
1
11
2
128
4
14
10
18
13
0.83
1.00
0.50
0.50
0.67
1.00
0.60
1.00
0.91
0.50
0.84
0.75
0.93
0.80
0.83
0.85
Mammals (Mammalia)
Bats (Chiroptera)
Carnivores (Carnivora)
Even-toed ungulates (Artiodactyla)
Insectivores (Insectivora)
Marsupials (Marsupalia)
Odd-toed ungulates (Perissodactyla)
Rabbits and hares (Lagomorpha)
Rodents (Rodentia)
100
15
14
4
8
1
0
7
51
32
2
5
1
4
0
2
1
17
132
17
19
5
12
1
2
8
68
0.76
0.88
0.74
0.80
0.67
1.00
0.00
0.88
0.75
36
16
19
1
19
7
10
2
55
23
29
3
0.65
0.70
0.66
0.33
355
110
465
0.76
Taxonomic group
Amphibians (Amphibia)
Frogs and toads (Anura)
Salamanders (Caudata)
Reptiles (Reptilia)
Lizards (Squamata: Sauria)
Snakes (Squamata: Serpentes)
Turtles (Testudines)
Total
PATRICIA N. MANLEY ET AL.
304
A wide variety of life-history characteristics appeared to be well represented by the species adequately
detected (Table 4). All four primary trophic groups
were represented equally well, although the approach
was variously effective in representing dietary subgroups of carnivores and herbivores (ranging from 50
to 82% of species adequately detected). The multiplespecies approach was predicted to adequately detect a
greater proportion of species with moderate, large, or
extensive home ranges (73–84%) compared to species
with small home-ranges (62%). Unlike home range
size, body mass did not appear to have any bearing on
sampling efficiency, given that similar proportions of
species were adequately detected in each body-mass
category. Terrestrial, semi-aquatic, and aquatic species
were about equally represented, with 64–79% of all
species being adequately detected in each category.
Some greater discrepancies in representation were
observed among ecological groupings (Table 4). Most
common species (90%) and almost all habitat generalists (98%) were adequately detected, whereas 68%
of habitat specialists and only 33% of rare species were
adequately detected. Over 70% of the rare species were
considered rare based on the small size of their ranges,
therefore there were few monitoring points within their
ranges. Exotics, endemics, and species of concern were
underrepresented relative to most of the other species
groups, with 53%, 24%, and 47% predicted to be adequately detected, respectively (Table 4).
DISCUSSION
Overall performance
Our evaluation revealed that a multiple-species monitoring approach has the potential to accomplish many
monitoring objectives widely shared among land-management agencies. Evaluation results indicated that this
approach can yield data on changes in occurrence for
a large number and breadth of species. The relatively
equitable representation of most ecological traits also
indicates that this monitoring approach can provide a
robust characterization of the sum total of all vertebrate
species. Although growing threats to biological diversity highlight the need for ecoregional-scale monitoring
systems, examples of cross-taxonomic-group monitoring systems are rare. A few large-scale monitoring
schemes have been designed for individual taxonomic
groups (e.g., Breeding Bird Survey for birds [Bystrak
1981, Droege 1990], North American Amphibian Monitoring Program for amphibians [Weir and Mossman,
in press]), but sampling designs tailored to one taxonomic group may have limited utility for other taxonomic groups. For example, road-based surveys such
as the Breeding Bird Survey have the potential to provide biased results for local areas because their placement is nonrandom and some taxa are affected by the
presence and impacts of roads (e.g., Mumme et al.
2000, Robitaille and Aubry 2000). A primary strength
Ecological Applications
Vol. 14, No. 1
of the multiple-species monitoring approach is its
cross-taxonomic construct, which relies on a systematic
or random sampling approach that is not biased toward
a single taxonomic group.
We believe that the assigned values for probability
of presence and detection provided a reliable portrayal
of the benefits associated with this monitoring approach. Although we assumed that suitable habitat was
occupied, the majority of species were assigned the
lowest probability of presence (pp 5 0.10), and even
if only 50% of all suitable habitat were occupied by
each species, our results indicate detections would remain adequate for a substantial number of species (n
ø 170 species). Probability of detection, on the other
hand, was based only on the primary method of detection even when additional sampling methods targeting other species would likely increase detection
rates. Also, presence at each sample point is accomplished by a single detection, making detection of species that occur at moderate to high densities relatively
certain. Overall, the multi-species monitoring approach
obtained an adequate number of detections for the majority of vertebrate species, and in many cases the number of monitoring points was two and three times the
number needed. Admittedly, desired levels of precision
are likely to be more ambitious (i.e., smaller effect sizes
and greater confidence and power) for some individual
species of interest. Our results indicate that even with
more ambitious objectives, a substantial number of species would be adequately detected, certainly more than
are currently considered feasible by most land managers.
Calculating change between two points in time may
not satisfy information needs for some species where
more detailed information on trends over time are desired. Given equivalent effect size and error rates, detecting trends generally requires a greater minimum
sample size than comparing two points in time (Copas
1988). The parameters we used to evaluate this approach were intended to reflect a sampling effort that
would meet the needs of a decade-long monitoring program with moderate assurance that changes in population status would be detected.
The cost and logistics associated with implementing
a multiple-species monitoring approach are not trivial.
However, preliminary field tests conducted in the Sierra
Nevada ecoregion (P. Manley, unpublished data) suggest that implementation is not only feasible, but reasonably cost efficient. For example, the combination
of point counts, small-mammal trapping, and area
searches could be considered a core suite of detection
methods for a multiple-species monitoring effort, given
the large number and range of species they detect. In
the Sierra Nevada, these three protocols were the primary detection method for 80% of the vertebrate species (Table 1). Based on the protocol specifications outlined in this paper and then used in our field test, it
cost a total of approximately $2800 per site to conduct
MULTIPLE-SPECIES APPROACH TO MONITORING
February 2004
305
TABLE 4. Predicted adequacy of the multiple-species monitoring approach in detecting $20%
relative change (with 80% confidence and power) in the occurrence of vertebrate species by
species group in the Sierra Nevada, California, USA.
No. of vertebrate species
Adequately
detected
Not adequately
detected
Total
Proportion
adequately
detected
199
52
9
76
62
62
13
2
30
14
261
65
11
106
76
0.76
0.80
0.82
0.72
0.82
2
74
80
46
1
28
5
0
23
25
12
1
10
2
2
94
105
58
2
38
7
1.00
0.76
0.76
0.79
0.50
0.74
0.71
Home range
Small
Medium
Large
Extensive
53
214
56
32
30
54
11
12
83
268
67
44
0.62
0.80
0.84
0.73
Body mass
Small
Medium
Large
303
25
27
95
7
8
398
32
35
0.76
0.78
0.77
Habitat type
Terrestrial
Semi-aquatic
Aquatic
297
47
11
86
21
3
383
68
14
0.78
0.69
0.79
Habitat specificity
Highly specific
Moderately specific
Generalist
227
88
40
108
2
0
335
90
40
0.68
0.98
1.00
Rarity
Rare
Common
38
317
76
34
114
341
0.33
0.90
Endemics and exotics
Sierra Nevada endemic
California endemic
Sierra Nevada exotic
3
2
10
6
10
9
9
12
19
0.33
0.17
0.53
Species of concern
18
20
38
0.47
Species group
Trophic level and diet
Carnivore
Vertivore
Piscivore
Invertivore
Insectivore
Scavenger
Omnivore
Herbivore
Grazer/browser
Frugivore
Granivore
Nectar-eater
point counts ($800), small-mammal trapping ($1500),
and area searches ($500), including data collection, entry, and analysis. As a point of reference, current costs
for the full suite of vegetation data targeted by the
national Forest Inventory and Analysis (FIA) program
are approximately $2500 per site (B. Rhoads, personal
communication). Further, the monitoring program for
the California Spotted Owl in the Sierra Nevada alone
is currently funded at approximately $1 million per year
(J. Robinson and S. Thompson, personal communication). This same level of funding would enable multiple-species data collection at approximately 13% of
the points in the Sierra Nevada, which exceeds the FIA
program’s 10% annual sample target (Schreuder et al.
2000). Thus, using existing monitoring efforts as a
benchmark, the cost of a multiple-species monitoring
approach at the ecoregional scale is within the fiscal
and institutional capacity of land-management agencies.
Enhancement options
One of the primary challenges in designing a multiple-species monitoring approach is to maximize the
number and range of species adequately detected. The
multiple-species approach was predicted to be successful in detecting occupancy change for over three
quarters of all vertebrate species. The variety of lifehistory and ecological groups evaluated in our analysis
was generally well represented. However, a monitoring
effort that omits 24% of all species, or the majority of
306
PATRICIA N. MANLEY ET AL.
rare species, may be unacceptable in some situations.
In these circumstances, a multiple-species monitoring
approach can be augmented to improve detections for
target species or species groups. Augmentations can
target one or more factors that affect probability of
observation: probability of presence, probability of detection, and the number of monitoring stations within
the range of a species. For example, probability of detection may be improved by increasing effort per protocol (effort per visit and number of visits) and adding
protocols. In addition, the number of monitoring points
(i.e., grid density) can be increased within a species’
range or throughout a region. However, such fieldbased augmentations may not always be effective at
improving detection rates for species with low densities
or limited geographic ranges in the region. In these
cases, either independent, species-specific monitoring
programs will need to be developed or imbalances in
representation among species groups can be dealt with
using statistical techniques (e.g., Efron 1983, Chambers and Hastie 1993).
A central design parameter, and also a limitation, of
the multiple-species monitoring approach is the reliance on presence–absence data. Recent literature reflects a growing appreciation for the contribution of
presence–absence data in addressing large-scale population-trend questions (e.g., Scott et al. 2002). In addition to providing trends in the proportion of sites
occupied by species, presence–absence data may be
analyzed in innovative ways to extract additional information about populations, such as comparing the
proportional occupancy of ‘‘sink’’ vs. ‘‘source’’ habitats as an index of change in abundance (Krohn 1992,
Bowers 1996). However, clearly binomial occurrence
data do not provide precise estimates of abundance. In
some cases, multiple-species detection methods readily
provide abundance estimates without additional field
effort. For example, relative abundance estimates can
be derived from point-count data without additional
field effort, and density estimates can be acquired by
distance sampling (Buckland et al. 2001). However,
estimates of abundance or density as monitoring metrics could require a greater sample size (Whittemore
and Gong 1991, Thompson 2002) than indicated by
binomial data.
Another potential limitation is that the multiple-species monitoring approach is retrospective in nature
(NRC 1995). It is an inductive approach that is valuable
for describing conditions and detecting undesirable
changes, but it is not designed to determine the causes
of changes nor is it tailored to monitor specific changes
that are expected. The alternative approach, predictive
monitoring, seeks to detect indications of undesirable
effects before they have a chance to become serious
(NRC 1995). It focuses on detecting changes expected
to result from actions or activities, referred to as
‘‘stressors’’ or ‘‘affectors’’ (Noon et al. 1999, Manley
et al. 2000). Predictive monitoring assumes a cause–
Ecological Applications
Vol. 14, No. 1
effect relationship between affectors and expected
changes, and is an efficient monitoring approach where
there is a high level of confidence about cause–effect
relationships. Its weakness is that assumptions about
cause–effect relationships may be inaccurate, effects
may have multiple causes, and ecological vulnerabilities may be unknown, resulting in a failure to detect
critical changes. In practice, retrospective and predictive monitoring are complementary and any large-scale
monitoring system would benefit from a combination
of both.
Collateral applications
Multiple-species monitoring data have an array of
potential applications to conservation and management. First, such an omnibus approach can provide data
on correlative relationships between species and environmental conditions, which in turn can be used to
build habitat-relationship models (e.g., Morrison et al.
1998, Carroll et al. 1999, Scott et al. 2002). Habitat
models are useful tools for conservation and land-management planning in that they can be used to estimate
the amount and location of suitable habitat for multiple
species, assess the potential effects of various management or conservation scenarios, and identify biological diversity hot spots and conservation priorities
(e.g., Scott et al. 1987, 1993, 2002, Noon et al. 1997).
Indeed, the development of reliable habitat models is
a critical need of land-management agencies that are
mandated to monitor habitat conditions for species of
concern (Verner et al. 1986, Stauffer 2002, Van Horne
2002).
Second, monitoring data could be used to test existing or potential indicator species by validating relationships between candidate indicators and the species or conditions they are assumed to represent. Typically, land managers select indicator species that are
intended to serve multiple objectives, such as species
of concern that are also closely associated with some
habitat type of interest. However, the process of validation (Committee of Scientists 1999), let alone monitoring, is rarely accomplished (e.g., GAO 1997). Validation processes are commonly envisioned as detailed
and costly efforts directed at individual species (e.g.,
Lint et al. 1999). A multiple-species monitoring approach offers the alternative of generating correlative
data to evaluate several proposed or existing indicator
species. For example, validation of umbrella species
(Berger 1997, Caro and O’Doherty 1999) could be accomplished by correlating habitat associations and population trends of candidate umbrella species with those
of species in the community being represented (e.g.,
Fleishman et al. 2000), as well as overall trends in the
characteristics of the represented community itself
(e.g., vegetation structure and composition, species
composition and diversity).
Data provided by a multiple-species monitoring approach could also be used to empirically derive indi-
February 2004
MULTIPLE-SPECIES APPROACH TO MONITORING
cator species a posteriori, unfettered by preconceived
notions of features that presage a strong indicator. This
approach to selecting indicators could rely on some
form of classical ordination (e.g., Hill 1979, Lebreton
et al. 1991, Dufréne and Legendre 1997) and would
reflect an adaptive form of monitoring. Through the
course of monitoring, the emphasis of monitoring could
shift as information-rich indicators are discovered and
integrated into the design of a monitoring program.
Finally, assessing patterns of change across species
and species groups could serve to validate ecosystembased conservation strategies. For example, a coarsefilter conservation approach that maintains a representative set of ecological communities is assumed to sustain all the components and processes that make the
ecosystem function (Wilcove 1993, Noss and Cooperrider 1994). Change data for a broad array of species
could serve to provide early evidence of the success
or failure of coarse-filter conservation efforts.
CONCLUSIONS
Our approach represents a ‘‘bet-hedging’’ strategy
that targets basic population data for a large number
and great breadth of species. Clearly, multiple-species
monitoring can be considered a promising complementary approach to those that focus on individual species of interest, including species of concern and indicator species. More notably it summons a shift in
thinking about options for obtaining information about
biological diversity and system function by monitoring
the status of many species. Any effort that relies solely
on a small set of indicator species will be subject to
skepticism given the history of misuse, overuse, and
poor performance of the indicator concept. However
as Simberloff (1998:248) stated regarding the objectives of indicator species approaches, ‘‘The reductio
ad absurdum of this confusion of goals is the proposition (Noss 1990) that we should monitor virtually
everything as indicators—a large group of species,
dominance-diversity curves, canopy height diversity,
percent cover . . . etc. . . . . Of course the absence of
resources to do all this measurement was the raison
d’etre for indicator species in the first place!’’ The
multiple-species monitoring approach represents a
middle ground in this argument and is a feasible solution to this management conundrum because it targets
the most basic of population data for a breadth of species at a feasible cost. It thus becomes practical to
consider monitoring many species. The range of benefits to be gained from a multiple-species monitoring
approach, as depicted in this simulation exercise, needs
to be corroborated through field testing. However, rapid
declines in biological diversity should compel us to
welcome novel approaches, move quickly to empirically test ones that have promise, and then refine and
implement those that prove effective.
307
ACKNOWLEDGMENTS
Many individuals contributed to various stages of the project and manuscript. Christina Hargis provided unflagging and
invaluable support throughout the effort. John Keane helped
develop the approach and contributed many insightful ideas,
considerations, and cautions. Michelle Mckenzie and Aaron
Bilyeu spent many long hours gathering species information
and building data bases. German Whitley and Tanya Kohler
provided GIS assistance. Kevin McKelvey and Christina Hargis reviewed and greatly improved the draft manuscript. The
USFS Pacific Southwest Region and Station provided funding.
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